Control strategies of_different_hybrid_energy_storage_systems_for_electric_vehicles_applications

Received March 5, 2021, accepted March 18, 2021, date of publication March 29, 2021, date of current version April 9, 2021.
Digital Object Identifier 10.1109/ACCESS.2021.3069593
Control Strategies of Different Hybrid Energy
Storage Systems for Electric Vehicles Applications
AMIT KUMER PODDER 1, OISHIKHA CHAKRABORTY 1, (Student Member, IEEE),
SAYEMUL ISLAM 1, NALLAPANENI MANOJ KUMAR 2,
AND HASSAN HAES ALHELOU 3,4, (Senior Member, IEEE)
1Department of Electrical and Electronic Engineering, Khulna University of Engineering & Technology, Khulna 9203, Bangladesh
2School of Energy and Environment, City University of Hong Kong, Hong Kong
3School of Electrical and Electronic Engineering, University College Dublin, Dublin 4, D04 V1W8 Ireland
4Department of Electrical Power Engineering, Faculty of Mechanical and Electrical Engineering, Tishreen University, Lattakia 2230, Syria
Corresponding authors: Hassan Haes Alhelou (alhelou@ieee.org) and Nallapaneni Manoj Kumar (nallapanenichow@gmail.com)
The work of Hassan Haes Alhelou was supported in part by the Science Foundation Ireland (SFI) through the SFI Strategic Partnership
Programme under Grant SFI/15/SPP/E3125, and in part by the UCD Energy Institute.
ABSTRACT Choice of hybrid electric vehicles (HEVs) in transportation systems is becoming more
prominent for optimized energy consumption. HEVs are attaining tremendous appreciation due to their
eco-friendly performance and assistance in smart grid notion. The variation of energy storage systems in
HEV (such as batteries, supercapacitors or ultracapacitors, fuel cells, and so on) with numerous control
strategies create variation in HEV types. Therefore, choosing an appropriate control strategy for HEV
applications becomes complicated. This paper reflects a comprehensive review of the imperative information
of energy storage systems related to HEVs and procurable optimization topologies based on various control
strategies and vehicle technologies. The research work classifies different control strategies considering
four configurations: fuel cell-battery, battery-ultracapacitor, fuel cell-ultracapacitor, and battery-fuel cell-
ultracapacitor. Relative analysis among different control techniques is carried out based on the control
aspects and operating conditions to illustrate these techniques’ pros and cons. A parametric comparison and
a cross-comparison are provided for different hybrid configurations to present a comparative study based on
dynamic performance, battery lifetime, energy efficiency, fuel consumption, emission, robustness, and so on.
The study also analyzes the experimental platform, the amelioration of driving cycles, mathematical models
of each control technique to demonstrate the reliability in practical applications. The presented recapitulation
is believed to be a reliable base for the researchers, policymakers, and influencers who continuously develop
HEVs with energy-efficient control strategies.
INDEX TERMS Electric vehicle, energy storage systems, battery, ultracapacitors, fuel cell, hybrid electric
vehicle, control strategy, vehicle topology.
NOMENCLATURE
AMT Automatic Manual Transmission
BERS Braking Energy Regeneration Strategy
BPNN Back Propagation Neural Network
DP Dynamic Programming
ECMS Energy Consumption Minimization Strategy
EMR Energetic Microscopic Representation
EMS Energy Management System
ESS Energy Storage System
EV Electric Vehicle
FC Fuel cell
The associate editor coordinating the review of this manuscript and
approving it for publication was Yang Li .
FLC Fuzzy Logic Control
HESS Hybrid Energy Storage System
HEV Hybrid Electric Vehicle
HEVS Hybrid Electric Vehicle System
HWDC Highway Drive Cycle
ICE Internal Combustion Engine
IFOC Indirect Field-Oriented Control
IUDC Indian Urban Driving Cycle
MPC Model Predictive Control
NEDC New European Driving Cycle
NYCC New York City Cycle
PFS Power Following Strategy
PI Proportional Integral
VOLUME 9, 2021
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
51865

A. K. Podder et al.: Control Strategies of Different Hybrid Energy Storage Systems for EVs Applications
PID Proportional Integral Derivative
PMP Pontryagin’s Minimum Principle
PSO Particle Swarm Optimization
SC Supercapacitor
SDP Stochastic Dynamic Programming
SFTP Supplement Federal Test Procedure
SOC State of Charge
TTCAN Time-Triggered Controller Area Network
UC Ultracapacitor
UDS Urban Dynamometer Schedule
I. INTRODUCTION
The world is moving towards an era full of facilities updated
with more recent inventions; pollutions are rising simultane-
ously where constant harm is evident to all living beings. Con-
ventional vehicles with an internal combustion engine (ICE)
contribute a lot to this issue resulting in the greenhouse effect
with a noticeable emission rate [1]. Hence, the idea of electric
vehicles (EVs) came up with different energy storage devices
such as batteries, supercapacitors (SCs) or ultracapacitors
(UCs), and fuel cells (FCs) [2]. Modification over these EVs
created hybrid electric vehicles (HEVs) as a combination
of the formerly mentioned sources with increased efficiency
and sufficient aid in ensuring uninterrupted power supply in
vehicles. Very intelligently, the emission rate of CO2 and CO
can get lessened by proper installation of HEVs. Battery life
can be successfully extended up to a satisfactory level; FC’s
longevity can now be found in an easier norm [3]. Researchers
have utilized different control strategies to provide real-time
application over a long distance [4].
Integration of UCs as an auxiliary power source in any
system helps reduce stress over battery or FC by taking care
of transient load conditions. The whole process runs under
the maintenance of state of charge (SOC) level for the regard-
ing sources injected, taking any new topology into account,
or sometimes as a strategy to control converters considered
in the system. Proportional integral (PI) controller [5], fuzzy
logic control (FLC) [6], model predictive control (MPC) [7],
rule-based control [8], wavelet-based control [9], linear mode
control [10], real-time performance-based control and many
more control strategies are being suggested in recent times.
Further research works on the betterment of HEVs are going
on.
A detailed classification of plug-in HEVs based on the
control logic being utilized and an overview of the controllers
is depicted in [11]. Each control strategy shows its con-
veniences and drawbacks. The trade-off between efficiency
and cost significantly affects the production and performance
efficacy of electric vehicle technology. The presented global
optimization methods reduce costs for multiple variables,
lessen emissions, and increase mileage. A critical study based
on various control strategies is presented in [12]. The analysis
identifies several uncertainties and complexities in terms of
the robustness of the electric vehicle technology. A lack of
proper direction for future work on several kinds of HEVs
composed of multiple energy storage systems (ESSs) with a
preferable outline of control strategies and energy manage-
ment schemes is stated in [11], [12]. An elaborated discus-
sion on optimal sizing of various ESSs that introduces the
modified particle swarm optimization algorithm to determine
the optimal sizing is presented in [13]. The study analyzes
numerous parameters, driving cycles, cost-effectiveness to
design an optimal ESS sizing for different configurations:
only Battery, only UC, and Battery and UC. A review analysis
of the present state of different ESSs of EV is done in [14],
including the battery classification, the battery’s current con-
dition, and the power charging capability. Although few bat-
tery technologies show high potential for providing superior
performance, experimentation over these technologies has
not been completed yet. Moreover, the trending lithium-based
EV battery having a restriction in energy density, limitation in
the life cycle, and high initial cost. More research needs to be
conducted for the betterment of the performance of EV bat-
teries [15]. A wavelet function-based indirect field-oriented
control (WT-IFOC) is proposed in [16] that varies different
parameters such as speed and steering angle input to verify
different test strategies’ efficacy. The controller illustrates a
smooth controlling platform to determine minimal peak over-
shoot, suitability in smooth propagation of EVs on the curved
road, and quick settling time over the PID (Proportional-
integral-derivative) controller under numerical consideration.
A comparative analysis is performed in [17] that shows a
relative estimation of the dynamic programming (DP) and
Pontryagin’s minimum principle (PMP) for the HEV. The
study includes the automatic manual transmission (AMT)
concept to analyze the trade-off of fuel consumption and the
gear shifting frequency. Again a research work in [18] illus-
trates a comparative study of different ESSs that indicates UC
for greater efficacy. The analysis also shows the production
cost of the formation of different types of batteries. Energy
density for hydrocarbons is seen much higher, but energy
efficiency is the lowest in this case. However, optimization
of EV’s efficiency and performance and its charging infras-
tructure is also suggested for making EV a viable choice in
transportation. EV’s current status, the large-scale develop-
ment process, EV’s sustainability in transportation systems,
different charging modes, communication technology, and
component maintenance are discussed in [19]. For example,
a set of significant challenges, safety limitations, overcom-
ing higher starting price of EVs, development in current
charging technologies, and increasing battery management
efficiency, are also presented. A comprehensive analysis of
different control strategies to characterize battery perfor-
mances under various situations is demonstrated in [20].
The research work considers multi-power sources, sizing
of ESS, stability, distributed networks to explore battery
performances.
The significance of hybrid ESS (HESS) over the individual
ESS in EV is highlighted in [21]. Integration of several ESSs
(such as UC, battery, and FC) enhances system stability,
charging-discharging rate, driving range, storage lifetime,
51866 VOLUME 9, 2021

TABLE 1. Contributions of the proposed research work in comparison to other research works.
and cost. An informative comparison among different control
techniques is presented in [22] that emphasizes structural
complexity and HEV optimization. The study includes
genetic algorithm, stochastic dynamic programming, energy
consumption minimization strategy, and neural network
method for comparative analysis of various schemes. Further-
more, battery-UC-based HESS’s main issues are discussed
in [23] that consider aging mechanism, state estimation, and
lifetime prediction. A comparative analysis for FC-UC-based
HESS is done in [24] to choose the optimal power allocation
strategy from the PID-based and rule-based approach [24].
The DP algorithm and a semi-physical experimental plat-
form are included to verify the two strategies’ effec-
tiveness. The experimental results indicate the rule-based
strategy is more efficient than the PID-based strategy. Again,
an improved power splitting strategy is proposed in [25] for
the hybrid propulsion system that utilizes DP and multiple-
grained velocity prediction to verify the proposed scheme
for different hybrid energy resources. The method forms
a semi-physical platform to maintain simulation activities
in hardware-in-the-loop simulation. An energy management
strategy for battery-FC-UC-based hybrid source vehicles is
also proposed in [26] that considers power capability as
significant parameters for battery and UC and utilizes finite
state machines. A rule-based power distribution strategy is
proposed in [27] for the hybrid power system. The process
includes the Bayes Monte Carlo approach to estimate the
lifetime of Battery and SC.
Therefore, for ensuring the convenience of getting into the
appropriate way to control different HEVs individually, this
paper upholds a detailed review of different existing control
strategies for optimized performance with suitable vehicle
topologies based on different ESSs with merits–demerits,
simulation, and experimentation capabilities. Table 1. illus-
trates the contributions of the proposed research work that
compare with other research works. The research work’s
significant contribution is categorizing the selected control
strategies in terms of their source configuration. The catego-
rization is further expanded into different terms considering
utilized techniques. The study presents a cross-comparison
among the utilized ESSs and the pros and cons of selected
control strategies in HEV. A comprehensive analysis of key
parameters, driving cycles, simulation platform, mathemati-
cal equations, and research work location is also presented so
that the researchers in this field can grasp the insights of HEV
and its numerous control strategies.
The rest of the paper consists of five more sections.
Section II and III present the fundamental information about
the commonly used energy storage devices in EVs and famil-
iarization with the hybrid configuration of different ESSs of
HEVs, respectively. Section IV notifies comparison among
the control strategies under discussion for the techniques
mentioned above. Finally, a positive outcome and the con-
clusion are drawn in sections V and VI, respectively.
II. COMMONLY USED ENERGY STORAGE DEVICES
FOR EVs
The commonly used energy storage devices are battery, FC,
and UC. They are used in EVs sometimes as primary energy
sources or sometimes as secondary energy sources when
utilized in hybrid mode. The mentioned sources are detailed
below with their generic model.
A. BATTERY
The battery is an excellent and widely used energy source
that can be found on every single electronic device. It acts
as a significant power source even in the HEV system. The
battery in HEVs is connected to a DC bus through a DC/DC
converter, and the battery voltage equals the DC bus voltage.
Battery SOC demonstrates an important concept that controls
the hybrid vehicle system’s behavior. The battery serves a
fast response to peak power during acceleration and restores
power during deceleration like UC. Figure 1 is a representa-
tion of different batteries that are common in HEVs. Figure 2
presents a battery model where a variable voltage source with
series resistance is employed. The battery voltage output can
be expressed as
Vbat = Ebat − Riibat (1)
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FIGURE 1. Commonly used batteries in HEVs.
FIGURE 2. Battery model [38].
where Vbat presents the battery output voltage, Ebat presents
the open-circuit voltage, Ri presents the internal resistance
and ibat presents the battery current. The battery voltage
dynamics can be expressed for both charge and discharge
cycles as















Ebat_dis = E0 − K
Q
Q − q
id − K
Q
Q − q
q
+ Mexp(−N ∗ q)
Ebat_ch = E0 − K
Q
q + 0.1Q
id − K
Q
Q − q
q
+ Mexp(−N ∗ q)
(2)
where E0 presents the constant voltage, id presents the filtered
current from low pass filter to battery current, K acts as the
polarization constant, Q is the maximum battery capacity, M
shows the exponential voltage, q is the extracted capacity, and
N is the exponential capacity.
The battery SOC can be expressed as
SOC = 100(1 −
R t
0 ibatdt
Q
) (3)
B. SCs/UCs
SC and UC are considered familiar energy sources in the
HEV system, but they differ in the length of electrodes and
storage capacity. Several research works named this source
FIGURE 3. (a) Basic construction of UC and (b) commonly used UC
modules in EVs.
FIGURE 4. Equivalent circuit of SC in MATLAB platform [40].
as UC while others as SC instead of UC. Authors remain
neutral in expressing UC and SC for each research work.
Battery and SC have the common goal to store charge, but SC
can store and discharge charge swiftly compared to the bat-
tery. The basic construction of SC in Figure 3(a) represents
two parallel electrodes separated by a small distance. When
an external voltage is applied to it, the negative electrode
stores positive ions, and the positive electrode stores negative
ions. SC provides transient power during acceleration and
restores power during braking while other energy sources
serve steady-state power to the HEV. Figure 3(b) represents
some UCs that are commonly used in HEVs.
The equivalent circuit of SC is shown in Figure 4, and
the SC output voltage can be presented by the Stem equation
51868 VOLUME 9, 2021

below.
Vsc =
NsQT d
NpNeεε0Ai
+
2NeNsRT
F
sinh−1

QT
NpN2
e Ai
√
8RTεε0c

− Rsc ∗ isc (4)
where QT =
R
iscdt. The self-discharge phenomenon can be
represented by modifying the electric charge of SC as follows
(isc = 0)
QT =
Z
iself _disdt (5)
iself _dis =













CT α1
1 + sRscCT
if t − toc ≤ t3
CT α2
1 + sRscCT
if t3 t − toc ≤ t4
CT α3
1 + sRscCT
if t − toc t4
(6)
where, α1, α2 and α3 present the constraints and the rate of
change of SC voltage during the time intervals (toc, t3), (t3, t4)
and (t4, t5) respectively.
C. FC
The FC converts chemical energy into electrical energy and
acts as the primary power source in HEVs. FC’s physi-
cal structure consists of an anode, cathode, and an elec-
trolytic membrane. The anode supplies hydrogen, and the
cathode supplies oxygen, as shown in Figure 5(a). Protons
pass through the electrolytic membrane, and electrons pass
through the load. FC provides a continuous steady-state
power supply to the HEV, but it cannot supply transient
power during acceleration and deceleration supplied by other
sources. FC proves itself as an efficient energy source with
no emission. Several FC types utilized in EVs are shown in
Figure 5(b).
Figure 6 presents a FC stack model. Two types of irre-
versible voltage drops are illustrated: activation overvoltage
and ohmic overvoltage. Due to the irreversible voltage drops,
the actual FC potential is less than the ideal FC poten-
tial. The output voltage of the FC is the combination of
the Nernst instantaneous voltage, activation overvoltage, and
ohmic overvoltage. FC output voltage can be expressed as
follows.
Vfc = ENernst + ηact + ηohm (7)
where Nernst instantaneous voltage ENernst = N[E0 +
RT
2F

PH2
√
Po2
PH2o

], activation overvoltage, ηact = −Bln(CIfc),
and ohmic overvoltage, ηohm = −IfcRint. By putting the
mentioned values in Eqn. (7), the output voltage of FC can
be rewritten as
Vfc = N

E0 +
RT
2F
PH2
√
Po2
PH2o
!#
−Bln CIfc

− IfcRint (8)
FIGURE 5. (a) Basic construction of FC [39] and (b) commonly used FC
modules in EVs. Note that PAFC: Phosphoric acid fuel cell, AFC: Alkaline
fuel cell, MCFC: Molten-carbonate fuel cell, SOFC: Solid oxide fuel cell,
SPFC: Solid polymer fuel cell, and DMFC: Direct methalon fuel cell.
FIGURE 6. FC stack model [38].
III. HYBRID ENERGY STORAGE SYSTEM (HESS) FOR EVs
HEVs are defined as vehicles using two or more energy
sources or storage such that at least one provides electrical
energy. For instance, a typical HEV contains an engine with
a fuel tank and an electric motor with a battery. HEV config-
uration can be subdivided into the following four types based
on the ESSs, and they are discussed below.
A. BATTERY-FC-UC
Such technology’s motto is the proper maintenance of the
energy distribution between its various sources for fulfilling
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the power demand at any mission requested. The configura-
tion reduces hydrogen consumption effectively and provides
an efficient performance during restless operation at a long
driving range as all the sources are available. The composi-
tion’s primary challenge is the battery current fluctuations,
the battery SOC level, and the storage capacity.
B. FC-UC
In this configuration, FC and UC are the sources of energy.
For FC, voltage is at the highest point during zero current
flow. It drops with the increasing current due to the activation
overvoltage and ohmic resistance losses in the membrane.
A sharp voltage drop occurs at a high current when the
reactant gases’ transport cannot follow the reaction’s amount.
FC should supply limited current. The slow power response
of FC can be compensated by the fast power response of UC
to maintain the specified performance of EV.
C. BATTERY–UC
The battery-UC configuration demonstrates a capable and
satisfactory energy system for EV that minimizes cost,
improves system’s reliability, and provides load-leveling
capability. The arrangement reduces energy losses and pro-
longs battery lifetime. UC assists the battery in reducing
stress during peak hours. The battery-UC HESS’s construc-
tional classification is presented in Figure 7, where the HESS
is primarily classified in three sectors: passive, semi-active,
and active. The semi-active structure is further categorized
into two sections: UC semi-active and battery semi-active,
and active topology is classified as parallel and cascaded
active topology.
FIGURE 7. The topological classification of battery-UC HESS [41].
D. BATTERY–FC
This technology shows FC working as the primary power
source and battery as a secondary support system. FC can
take care of higher load demand due to increased energy
density properties to charge up the associated battery module.
If the amount of fuel reaches near the permissible lower
limit, the battery also comes into action to aid the system in
continuing a sustainable performance. Thus efficiency gets
increased.
The schematic representation of the four HESS mentioned
above is shown in Figure 8. Table 2 demonstrates a paramet-
ric comparison among FC-UC-Battery, FC-UC, UC-Battery,
and FC-Battery configuration. The table illustrates a rela-
tive comparison of different terms: key parameters, initial
load demand, transient response, battery SOC, driving cycle,
simulation platform, and so on. Table 3 represents a cross-
comparison among the four configurations mentioned above
that emphasizes presenting relative information.
IV. CONTROL STRATEGIES FOR DIFFERENT HESSs
A. CONTROL STRATEGIES FOR BATTERY–UC HESSs
1) MPC STRATEGY
An effective MPC strategy is proposed in [42]. This strat-
egy comprises the backpropagation neural network (BPNN)
and PMP that focuses on economic fuel consumption and
lengthen the battery’s longevity. The MPC applies the BPNN
algorithm to predict vehicle speed and running characteristics
at different driving modes. PMP diminishes the estimation
load and saves time. The real-time optimization scheme is
implemented in MATLAB/ Simulink environment.
The system maintains the impartiality between complex-
ity, performance, and economy by considering the following
equation.
Pb + ηPsc = Pd (9)
Pb and Psc represent the output of battery and SC, respec-
tively, Pd is the demand power, and η is the converter’s
coefficient. The SOC of battery and UC are expressed by the
Eqns (10) and (11), respectively.
SOCb = −
Ib
Q
= −
Vb −
q
V2
b − 4RbPb
2RbQ
(10)
SOCsc
= −
SOCscVsc,max −
q
SOCscVsc,max
2
− 4RscPsc
2RscCscVsc,max
(11)
where, SOCb and SOCsc are the SOC of battery and SC,
respectively and Vb, Rb, and Q represents open-circuit volt-
age, equivalent internal resistance, and the rated capacity of
the battery, respectively. Vsc,max, Csc and Rsc present the max-
imum rated voltage, the capacity of the SC, and equivalent
internal resistance, respectively. The schematic diagram of
the MPC-based HESS is illustrated in Figure 9(a).
Another MPC strategy is proposed in [43], as shown in
Figure 9(b), which considers the fast response of UC and
the battery’s comparatively slow response for extending the
battery lifetime. It maintains the battery and UC SOC as well
as voltage at a reference level. The strategy maintains the total
required input current by the below Eqn. (12).
itotal req =
ptotal req
vbus
(12)
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FIGURE 8. A schematic representation of HEV system for (a) FC-battery-SC HESS configuration (b) FC-SC HESS configuration (c) Battery-SC HESS
configuration (d) FC-battery HESS configuration. Note that SC is also known as UC.
FIGURE 9. Representation of schematic diagram of battery-SC HEV for (a) MPC strategy proposed in [42], and (b) MPC strategy proposed
in [43].
where ptotal req is the required power that is supplied or stored
by the power source and vbus presents the constant DC bus
voltage.
A comparison between the mentioned two existing MPC
strategy for battery-UC HESS is presented in Table 4 based
on the control aspects, operating conditions, and their appli-
cations.
2) CONTROL STRATEGY FOR SEMI-ACTIVE BATTERY-UC
TOPOLOGY
A modified semi-active topology is presented in [41]. The
proposed configuration implies a peak current control that
assures a stable DC voltage and lessens the current fickleness.
A bidirectional DC/DC converter is utilized in the control
scheme. The converter has three separate operating modes:
standalone mode, boost mode, and buck mode. The configu-
ration focuses on an efficient regulation of DC voltage and
the reduction of overall cost by reducing the components’
size. Validation is committed through the dSPACE-1103 con-
troller board implemented by MATLAB/Simulink software.
Reference [44] presents another semi-active topology having
the advantages and utility of employing an SC in an HEV.
SC provides transient power during acceleration, restores the
loss of power during deceleration, and lessens the battery’s
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TABLE 2. The parametric comparison among FC-Battery, FC-UC, Battery-UC, and FC-Battery-UC topology.
pressure. The average power, pave from the initial time to final
time can be expressed as
pave =
1
t
Z t
0
px(t) dt (13)
where px(t) represents the instantaneous power supplied by
Battery or SC.
3) REAL-TIME ENERGY MANAGEMENT STRATEGY (EMS)
Real-time energy management is proposed in [45] that deter-
mines real-time solutions on various driving cycles. Three
standard driving cycles are considered: WLTC class2, NEDC
with urban and highway parts, and ARTEMIS. PMP is
applied to lessen the adaptation problem. It is compared
with other controls like filtering and DP to verify the pro-
posed scheme’s pertinence. A reduced-scale power hardware-
in-loop (HIL) simulation platform is utilized to verify the
experiment. Another improved real-time power-split control
strategy is proposed in [46]. It is a small-scale experimen-
tal platform that focuses on improving power management
through various sources and components performance. This
concept is evaluated using MATLAB/Simulink environment.
Three operating modes are presented: starting and accelera-
tion mode, constant speed mode, and deceleration or braking
mode. A three-wheel vehicle under Indian road conditions
is demonstrated to implement this strategy. This strategy
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TABLE 3. The cross-comparison among the four different HESSs for EVs.
TABLE 4. Comparison among two existing MPC strategies for Battery-UC HESSs.
reduces the RMS current and thus advances the battery per-
formance and lengthen the battery lifetime. The reference
current of battery and UC can be computed as follow
Iref =
pUC
vUC
(14)
Ibatt_reg =
pdemand
vbatt
− Ireg (15)
where Iref and vbatt represents the reference current
and battery terminal voltage respectfully and pdemand
presents the power demand. pUC is the power supplied or
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FIGURE 10. A schematic diagram of battery-SC-based HEV using rule
based EMS [48].
stored by the UC and vUC represents the voltage across
the UC.
Another real-time control strategy is presented in [47].
It provides step-by-step problem-solving tools that target
multi-objective optimization problems and then reformulate
the issues using weighted no-preference method. The final
step of this approach recommends the Karush-Kuhn-Tucker
method to formulate the solutions. The experiment is sim-
ulated using an advanced vehicle simulator (ADVISOR).
Simulation results show a prolonged battery lifetime and
excellent power distribution between sources. The efficiency
η of this approach is expressed as
η =
R
pload (t) d (t)
R
pload (t)d (t) +
R
ploss (t)d (t)
(16)
where pload (t) represents the real-time power demand of the
load and ploss represents the real-time power loss of the
system.
Table 5 presents a comparative analysis of three existing
real-time control strategies for battery-UC HESS.
4) RULE-BASED EMS
A rule-based EMS is proposed in [48] that is a semi-active
hybrid topology. It exhibits the attributes of the UC and pro-
longs the battery lifetime. The two driving cycles tested in this
topology are the USA Urban Dynamometer Schedule (UDS)
and the New European Driving Cycle (NEDC).
At first, a current controller is used to manage a stable
current flow between the sources. Then a voltage controller is
apprised to control the SC SOC within a reference limit. The
total power required by the vehicle pveh can be presented a
pveh = proll + paer + pslope + pacc (17)
where proll is the rolling resistance power, paer is the aero-
dynamic drag power, pslope is the slope resistance power
and pacc presents the acceleration resistance power. The
schematic representation of the proposed EMS is presented in
Figure 10.
The aerodynamic drag power can be described as
paer =
1
η
caer Aaer
76140
µ3
eh (18)
The rolling resistance power can be expressed as
proll =
µveh
η
MgfCos(α)
3600
(19)
The slope resistance power can be expressed as
pslope =
µveh
η
MgSin(α)
3600
(20)
The acceleration resistance power can be represented as
pacc =
µveh
η
δM
3600
dµveh
dt
(21)
The total current demand can be expressed as
Iload =
pveh
Ubus
(22)
where η is the drive efficiency, M is the mass of the vehicle, f
is the rolling resistance coefficient, g is the gravity constant,
α is the road slope angle, µveh is the vehicle velocity, Ubus
presents the bus voltage.
The last stages imply the other two steps like SOC of
the sources and modes of operation. The implementation
results in MATLAB/Simulink show a comparatively longer
battery lifetime and minimum costing. A scale factor αSOC is
calculated in this strategy to keep a balance condition between
battery SOC and SC SOC.
αSOC =
SOCUC High−SOCUC Low
SOCUC Low
SOCB High−SOCB Low
SOCB Low
(23)
Another rule-based power split strategy is proposed in [49].
This unit is tested in different driving cycles: Manhattan (low-
speed transit bus operation) and UDDSC (high-speed bus
operation).
Three stages are demonstrated. In the first stage, the
different modes of operation (charge or discharge) are con-
sidered. In the next steps, some parameters are referred to in
terms of the SOC of the source and operating modes. The
mentioned two existing rule-based control strategies are com-
pared in Table 6 based on their objectives, control aspects, and
driving cycles.
5) New Battery-UC EMS
For maintaining a stable DC operating voltage and SOC of
battery and UC, a strategy is proposed in [50], which uses
a small DC/DC converter compared to others. It presents a
small-scale test that is implemented and validated in PSAT
software. The experiment represents four operating modes:
(i) vehicle low constant speed operation, (ii) vehicle high con-
stant speed operation, (iii) acceleration, and (iv) deceleration.
Furthermore, for improving the power management between
sources and SOC of the battery and SC, a strategy is proposed
in [51]. It is tested in an Urban Driving Cycle-ECE-15 and
51874 VOLUME 9, 2021

TABLE 5. Comparison among three existing real-time control strategies for Battery-UC HESSs.
TABLE 6. Comparison between two existing rule based control strategies for Battery-UC hybrid energy systems.
validated using MATLAB/Simulink software. The simulation
result shows a stable DC operating voltage and enhanced
battery life. It considers two operating modes: motor mode
and regenerative braking mode.The strategy follows energy
conversion law like other strategies, and the load power Pload
can be expressed as
pload = pbat + psc (24)
pload =
1
η
Ftev (25)
where, η is the system efficiency, Fte is the total tractive
effort, v is the vehicle speed. The schematic diagram of the
proposed control scheme is represented in Figure 11(a). The
mentioned two strategies are compared in Table 7.
6) PARTICLE SWARM OPTIMIZATION (PSO)-BASED
STRATEGY
A significant EMS-based PSO algorithm is proposed
in [52] that ascertains the optimal power flow between
sources. ECE-15 urban driving cycle is tested using MAT-
LAB/Simulink and justifies battery/UC SOC and power dis-
tribution requirements. The PSO is a randomized scheme
that is influenced by the behavior of fish schooling and bird
flocking, and all follow the relation as presented below.
Veld
i (t + 1) = wVeld
i + C1R1 (t)

pd
best (t) − pd
i (t)

+ C2R2(t)(gd
best (t) − pd
i (t)) (26)
pd
i (t + 1) = pd
i (t) + Veld
i (t + 1) (27)
where, Veld
i (t + 1) is the particle velocity, pd
i is the particle’s
position, w is the weighted factor, C1, C2 are the acceleration
coefficients and R1, R2 presents the random variables.
7) FREQUENCY VARYING FILTER STRATEGY
The frequency varying filter strategy is presented in [53],
which reduced scale approach aims to confirm the efficient
power distribution between the sources by constructing a
VOLUME 9, 2021 51875

FIGURE 11. Representation of schematic diagram of battery-SC hybrid vehicle for (a) a power management strategy [51] (b) Frequency varying filter
strategy [53] (c) Current control and filter decoupling technique based control strategy [54].
TABLE 7. Comparison among two existing control strategies for Battery-UC HESSs proposed in Ref. [50] and [51].
three-layer EMS. There exist different operating modes: nor-
mal mode, charge mode, regenerative mode, and error mode.
The three-layer EMS provides a chopper level controller,
power-sharing controller, and energy state controller. The
process simulated by MATLAB/Simulink enables the SC
to supply transient power and confirm the fast responses.
51876 VOLUME 9, 2021

Figure 11(b) illustrates the schematic representation of the
proposed strategy.
8) CURRENT CONTROL AND FILTER DECOUPLING
TECHNIQUE-BASED CONTROL STRATEGY
A simplified power management strategy is proposed in [54]
based on current control and a filer decoupling technique that
confirms the battery to be less stressed and enables the SC
to respond at the fast dynamic condition like acceleration,
deceleration. This control scheme can involve FLC or neural
network for complex implementation. The schematic repre-
sentation of the control strategy is presented in Figure 11(c).
This strategy uses a half-controlled controller and the ECE-
15 European driving cycle. 1
τ is considered as the decoupling
frequency, and tmax is the time to achieve maximum discharge
current. The range of decoupling frequency in this approach
is considered by the following Eqn. (28).
2.2
tmax

1
τ

2.2
0.1tmax
(28)
9) FUZZY LOGIC-BASED CONTROL STRATEGY
A real-time EMS is presented in [55], which is composed
of FLC and filtering that intends to sustain the battery’s
peak current at a reference level and keep up the stable
voltage of UC. Three different driving cycles are introduced
to continue the action: The new European driving cycle
(NEDC), the Highway driving cycle (HWDC), and the Indian
urban driving cycle (IUDC). The experimental outcome
exhibits a controlled SOC of SC and a stable dc operating
voltage.
Another FLC-based EMS is presented in [56], which aims
to monitor the power fickleness under load variation. This
approach considers SOC of battery instead of UC and takes
appropriate steps following the change of SOC. The system is
simulated using Simulink environment and shows the charac-
teristics under various changes. The system has three inputs
and one output. The proportion coefficient of battery power
can be expressed as
Kbat =
Pbat
Preq
(29)
where Preq is the required power of the load and Pbat presents
the supplied power of the battery. The schematic represen-
tation of the proposed FLC-based EMS is demonstrated in
Figure 12.
The comparison of the two existing FLC strategies is pre-
sented in Table 8.
10) FASTER JOINT CONTROL STRATEGY
A faster joint control strategy is proposed in [57] that exhibits
a photovoltaic-based DC grid system that intends to govern
the effective power drift through various sources compared
to conventional methods. The experiment is tested on a small
scale and a large scale platform and is implemented in the
MATLAB environment. This process aims to reduce the
FIGURE 12. Representation of schematic diagram of battery-SC hybrid
vehicle for fuzzy logic based control strategy [56].
battery’s pressure with long battery life and quickly respond.
The total power flow in the DC-link is expressed as
Pl (t) − Pren (t) = Pb (t) + Psc (t) = Pavg + Ptran (30)
where Pl(t), Pren(t), Pb(t), Psc(t) represents the load power,
renewable energy source power, battery power, and SC
power, respectively. Pavg is the average power and Ptran
presents the transient power.
Pavg(t) + Ptran(t) = vdcitot (31)
The total current itot(t) can be represented as
itot (t) = iavg (t) + itran (t)
= Kp_vdcver + Ki_vdc
Z
ver dt (32)
where Kp_vdc and Ki_vdc present the proportional and inte-
gral constants of the voltage control loop. ver , vdc and vref
present the error voltage, dc-link measured voltage, and
DC-link reference voltage, respectively. Figure 13 (a, b) rep-
resents the schematic representation of the proposed control
strategy.
11) RULE BASED AND MPC BASED CONTROL STRATEGY
A system that combines both the rule-based control and the
model predictive control is proposed in [58], focusing on
the favorable power economy and enhancing battery lifetime.
When the power requirement is significant, and sources have
much energy, the MPC is employed. Otherwise, rule-based
control is applied. There are three driving cycles: ECE cycle,
UDDS cycle, and HWFET cycle to run the experiment. The
MATLAB simulation platform is used to justify the proposed
requirements.
12) OTHER CONTROL STRATEGIES
An effective way of controlling the power flow between
sources and loads is proposed in [59] to lessen the estimation
cost. A half-bridge topology is designed to run the experi-
ment. Frequency division between high and low demonstrates
VOLUME 9, 2021 51877

TABLE 8. Comparison between two existing fuzzy logic based control strategies for Battery-UC HESSs.
FIGURE 13. Representation of schematic diagram of battery-SC hybrid vehicle for (a) and (b) Faster joint control strategy [57], (c) (d) Quasi-Z-Source
Topology [60].
that high frequency is attenuated and hence enlarges the bat-
tery lifetime. Again the output voltage can be well regulated.
Again, a quasi-Z-source topology is presented in [60]
that emphasizes the appropriate power distribution between
sources under various operating conditions. This process
implies three modes: traction mode, where the battery and
UC supply power to the motor; regenerative mode, where
the inverse process of traction mode occurs and UCs energy
recovery method, where battery supplies power to the UC and
motor. To improve the efficiency of the proposed topology,
the total shoot-through current Ist is computed as
Ist =
P0
VUC
[2 −

2 +
VUC
Vb

Pb
P0
] (33)
51878 VOLUME 9, 2021

where P0 and Pb are the output power and battery power,
respectively. The schematic representation of the proposed
scheme is illustrated in Figure 13 (c, d).
The average shoot-through current of the switches in each
leg be
Iavss = −

2 +
VUC
Vb

P0
3VUC

kpower +
2P0
3VUC
(34)
Kpower is the power division ratio and can be described as
Kpower =
Pb
P0
(35)
The range of Iavss can be represented as
|Iavss| ≤
2P0
3VUC
(36)
Again, reference [61] presents a strategy considering com-
ponents sizing, energy consumption, and battery lifetime.
To minimize the global losses of the process, the current
ih_batt_ref is computed.
ih_batt_ref =
i(Rscp + RL2)U2
batt_0
(Rbatt + RL1) U2
scp_0
+ (Rscp + RL2)U2
batt_0
(37)
where Rbatt, Rscp are the equivalent resistance, RL1 and RL2
are the resistance of inductances L1 and L2 respectively.
Ubatt_0 and Uscp_0 present the voltage sources.
Vehicle acceleration strategy implies the estimation of
acceleration for proper functioning. This strategy determines
the traction force Ftract which is the combination of global
resistive force Fres and vehicle acceleration force Facc.
Ftract = Facc + Fres (38)
where Facc = Maveh
Ptract = Pacc + Pres (39)
where Pacc = vvehFacc
Pres = vvehFres (40)
M is the mass, vveh is the velocity and aveh presents the
acceleration of the vehicle.
The filtering strategy emphasizes the cutoff frequency. The
variable saturation current approach limits the power flow
through the battery that influences the overall performance.
Each of the methods is evaluated in ECE urban driving cycle
and implemented using MATLAB/Simulation environment.
The overall comparison among different control strategies
for batter-UC configuration is presented in Table 9. The
advantages and disadvantages of different control strategies
for this configuration are represented in Table 10.
B. CONTROL STRATEGIES FOR FC-BATTERY HYBRID
ENERGY SYSTEMS
1) NOVEL RANGE EXTENDED STRATEGY
A novel range-extended strategy is demonstrated in [62]. The
process is based on increasing the battery SOC to lessen vehi-
cle drivers’ anxiety. The FLC is introduced, which prevents
the rapid diminution of battery SOC and eventually increases
the battery SOC. An urban driving cycle (four ECE-15) is
applied to examine the approach. The implementation is con-
tinued via the MATLAB/Simulink platform, which achieves
a spiffy performance in battery SOC and fuel consumption.
Figure 14(a) presents the schematic representation of the
proposed control scheme.
2) OPTIMAL DIMENSIONING AND POWER MANAGEMENT
STRATEGY
An optimal dimensioning and power management strategy
is represented in [63]. This approach applies convex pro-
gramming to improve the optimal power management and
component sizing of the HEVs. ADVISOR is used as a sim-
ulation platform at different driving conditions: 1. Standard
Manhattan bus cycle 2. Standard city-suburban cycle and 3.A
real bus line cycle. This concept shows a better fuel economy
and component size maintenance.
3) POWER MANAGEMENT AND DESIGN OPTIMIZATION
STRATEGY
Power management and design optimization strategy are pre-
sented in [64]. It is a sub-system scaling model that ensures
optimal power management between sources. Stochastic
dynamic programming (SDP), which considers battery SOC,
is applied at the beginning of the experiment, and due to
some limitations, a Pseudo SDP controller is used. The test
is verified in three different driving cycles: FTP-72, HWFET,
and ECE-EVDC and demonstrates a good fuel economy. The
schematic representation of the proposed control strategy is
demonstrated in Figure 14(b).
4) OPTIMAL VALUE CONTROL STRATEGY
An optimal value control strategy is introduced in [65], based
on a Time-Triggered Controller Area Network (TTCAN).
It combines both the equivalent consumption minimization
strategy (ECMS) and the braking energy regeneration strat-
egy (BERS). TTCAN recognizes real-time problems and can
figure out the solution. This scheme was successfully applied
at the Beijing Olympic Games of 2008 and tested at ’China
city bus typical cycle.’ ECMS and BERS ensure better fuel
economy than other strategies, and also BERS achieves more
than ECMS.
5) RULE-BASED ENERGY MANAGEMENT STRATEGY
A rule-based energy management strategy is demonstrated
in [39] that comprises a classical PI control and focuses
on reducing fuel consumption. MATLAB/Simulink simu-
lation platform is applied in the European urban cycle
ECE-15. Three different modes are available: stop mode,
traction mode, and braking mode. Figure 14(c) illustrated the
schematic diagram of the proposed EMS.
6) POWER STRATEGY FOR HYBRID LOCOMOTIVE SYSTEM
A power strategy for a hybrid locomotive system is presented
in [66]. This strategy proposes a locomotive system that
VOLUME 9, 2021 51879

TABLE 9. Comparison among different control strategies for Battery-UC configuration.
51880 VOLUME 9, 2021

TABLE 9. (Continued.) Comparison among different control strategies for Battery-UC configuration.
FIGURE 14. Representation of schematic diagram of FC-battery HEV that utilized (a) Fuzzy control block for novel range extend strategy [62],
(b) Power management and design optimization strategy [64] (c) Rule based energy management strategy [39], and (d) Power strategy for
hybrid locomotive system [66].
is tested using two different techniques: power following
strategy (PFS) and fuzzy logic power management strat-
egy (FMS). The basic idea of the locomotive system is to
achieve the minimization of fuel consumption and dynamic
responses. The PFS method is first applied in different driv-
ing cycles, and then the FMS is introduced to justify the
VOLUME 9, 2021 51881

TABLE 10. Advantages and limitations of the mentioned control strategies for Battery-UC configuration.
51882 VOLUME 9, 2021

TABLE 10. (Continued.) Advantages and limitations of the mentioned control strategies for Battery-UC configuration.
requirements. The whole process’s implementation is eval-
uated using ADVISOR software and MATLAB/Simulink
environment. The simulation results in a comparative anal-
ysis between the two control units and shows a better FMS
performance. Figure 14(d) presents the schematic diagram
of the power control strategy.
Table 11 presents the relative analysis of different control
strategies for FC-battery configuration.
VOLUME 9, 2021 51883

TABLE 11. Comparison among the above-mentioned control strategies for FC-Battery configuration.
C. CONTROL STRATEGIES FOR FC–UC HYBRID ENERGY
SYSTEM
1) ENERGY SHARING STRATEGY
An energy sharing strategy is presented in [68]. A small-scale
experiment considering FC as the primary source and SC as
the secondary source is recommended in this approach. This
process concerns maintaining DC voltage at a specific limit.
The FC operates at steady-state conditions while SC delivers
peak power in the transient situation. An implementation is
done to validate this process for only an FC with the DC bus,
and another is done for FC and SC. MATLAB/Simulink and
ControlDesk software are included as the simulation platform
of this approach.
2) NON-LINEAR CONTROL STRATEGY
A nonlinear control strategy is illustrated in [69]. This non-
linear control is suggested using two sources: FC and SC,
to control hybrid electric power systems under the desirable
power supply. The proposed method introduces the Lyapunov
stability design techniques for the nonlinear analysis of the
system. The study is authenticated in a numerical simulation
platform using MATLAB software. Simulation in a con-
trolled regulation of DC operating voltage and SC reference
current and the system stability. The schematic representation
of the control strategy is illustrated in Figure 15(a).
3) ENERGETIC MICROSCOPIC REPRESENTATION (EMR)
AND INVERSION BASED CONTROL
An EMR and inversion-based control is presented in [70].
This strategy proposes an inversion-based small-scale test
platform using EMR techniques. It provides better conduct
compare to the classical approaches because it considers
saturation management. The SOC of UC at low and high
states is taken into account to ensure proper power manage-
ment. EMR, a graphical descriptive tool, presents smooth
power distribution from sources under load changes, and the
51884 VOLUME 9, 2021

FIGURE 15. Representation of schematic diagram of FC-SC hybrid vehicle for (a) Non-linear control strategy [69], (b) Power management and hydrogen
economy based strategy [71] (c) Wavelet approach based energy management strategy [72].
analysis is corroborated using MATLAB/Simulink environ-
ment showing a reliable and efficient method.
4) POWER MANAGEMENT AND HYDROGEN ECONOMY
BASED STRATEGY
An EMS is represented in [71] that focuses on power man-
agement and the hydrogen economy to reduce costs.
The driving cycle implies three modes: traction mode,
steady speed mood, and braking mode. The simulation
completed by MATLAB environment demonstrates an opti-
mal hydrogen economy of FC and maintaining refer-
ence power constraints. The load power Pload is defined
as
Pload = Pfc + Psc (41)
where Pfc and Psc are the FC power and SC power, respec-
tively. Figure 15(b) illustrates a schematic diagram of the
proposed control strategy.
5) WAVELET APPROACH BASED ENERGY MANAGEMENT
STRATEGY
An efficient EMS is given in [72] that includes a wavelet
approach to ensure proper power distribution from sources.
The schematic diagram of the proposed strategy is demon-
strated in Figure 15 (c). FC provides low power, whereas
UC offers high power. The ECE-15 driving cycle and real
driving cycle are introduced for the experimental simulation
in MATLAB/Simulink, SimPowerSystem toolbox. This strat-
egy contributes to a stable performance under load variations
and can take stress under overload conditions. The load power
VOLUME 9, 2021 51885

Pload can be expressed as
Pload = ηFC PFC (t) + ηUC PUC (t) (42)
where ηFC and ηUC presents the efficiencies of converters of
FC and UC, respectively.
Pload = V[Cr Mg cos (α) + Mg sin (α)
+ M
dv
dt
+
1
2
ρsCxV2
] (43)
where Cr and Cx illustrates the friction and aerodynamic
coefficients, respectively. ρ and s represents the air density
and the front surface area, respectively. M is the mass, g is
the gravity constant, and α is the slope angle. The continuous
wavelet transform can be described as
CWTψ
x (τ, s) = ψψ
x (τ, x)
=
1
√
|s|
Z
x (t) ψ∗
(
t − τ
s
)dt (44)
where τ and s are the translation and scale parameters, respec-
tively. ψ (t) is called the mother wavelet.
6) LINEAR AND SLIDING MODE CONTROL BASED STRATEGY
Linear and sliding mode control are presented in [73], showin
a stable and smooth vehicle system under load changes.
Sliding mode control is applied here to assure security and
to facilitate power management. Three modes are defined to
ascertain proper power management depending on the SC
SOC and power demand as follows
In normal mode, ISCref = II0 − IFC (45)
In discharge mode, ISCref = II0 − IFCmax (46)
In charge mode, ISCref = II0 + IFCmax (47)
where ISCref presents the reference current of the SC. MAT-
LAB/Simulink is used as a simulation platform that confirms
the required performances.
7) HYBRID EMS
A hybrid EMS is presented in [74]. This strategy implies three
different methods: voltage control, average current control,
and hybrid control, where hybrid control is the combination
of the other two techniques. The voltage control method
responds to the reference voltage under high-speed variations.
The reference signal of the converter Vdc_ref can be calculated
as follows:
Vdc_ref = Vdc_max − ωm(K1) (48)
where K1 =
Vdc_max − Vdc_min
ωmax
(49)
Vdc can be computed from
Vdc =
1
C
Z t
0
iscdt (50)
Average current control maintains average power under slow
speed variations. The average reference current Iav is derived
from
Iav =
Pav
Vdclink_av
(51)
therefore
Iav =
R t
0 Pinstmechdt
R t
0 Vdclinkdt
(52)
Hybrid control includes both control schemes and operates
only one control scheme depending on low or high speed.
Table 12 represents a comparative analysis among different
control strategies for FC-UC configuration.
D. CONTROL STRATEGIES FOR FC-BATTERY–UC HYBRID
ENERGY SYSTEM
1) MPC
An MPC method for the FC-Battery-UC system is presented
in [77]. With the ability to maintain the reference current
to a specific range using a hysteresis control, this proposed
control strategy provides flexibility and ensures energy effi-
ciency. FC and battery act as the primary and secondary
sources, respectively, while additional energy is provided
by a UC that provides peak power during accelerating and
braking of the HEV. This strategy, a feedback control system,
focuses on utilizing the energy supplied by HES and main-
taining a real-time solution for the optimization problem. DP,
an effective tool for determining a dynamic system’s optimal
solution, presents advantages for the unconstrained MPC.
A small-scale experiment is involved and validated in this
control strategy. The whole process is implemented using
MATLAB/Simulink software on a dSPACE platform repre-
senting responses to various changes. The process involved
in this strategy results in an efficient energy management
control strategy with excellent dc bus voltage regulation.
2) PARALLEL ENERGY-SHARING CONTROL STRATEGY
A parallel energy-sharing control strategy is demonstrated
in [78] that concerns the proper energy management and
power flow through the power sources. It is capable of main-
taining the required power supply. The system is designed
with six control loops for three reasons: maintaining proper
DC voltage, state of charge of energy sources, and the current
flow.
Each of the power sources is connected with the DC bus
in a parallel manner. The system uses six control loops: three
inner current control loops, a DC bus voltage control loop,
a battery charge control loop, and a UC voltage control loop.
UC can provide peak power requirements during the acceler-
ating and braking situation of the electric vehicle. The battery
can not respond to the peak power requirements like UC and
operates at a safe range of power. This strategy is imple-
mented using MATLAB/Simulink simulation environment to
validate different conditions like start-up, acceleration, and
deceleration. The simulation results in excellent responses
to the vehicle’s torque control and protects the FC and the
battery from overstressed. Reference signals at each control
51886 VOLUME 9, 2021

TABLE 12. Comparison among the above-mentioned control strategies for FC-UC configuration.
loop can be illustrated as below:
Vbus ref = constant (53)
IUC ref = Iload − IFC feedback + IBatt feedback

+ IUC−D (54)
IBatt ref = Iload + IUC−C − IFC feedback (55)
IFC ref = Iload + IBatt−C + IUC−C (56)
where Vbus ref is the dc bus voltage, IFC ref , IUC ref and
IBatt ref present the current loop reference signals in the FC,
UC, and Battery, respectively. IFC feedback and IBatt feedback
presents the feedbacks current through the FC and bat-
tery, respectively. Iload presents the load current demand.
VOLUME 9, 2021 51887

FIGURE 16. Representation of schematic diagram of FC-SC-battery hybrid vehicle for (a) Parallel energy-sharing control strategy [78] (b) Wavelet and
FLC [80].
IUC−C and IUC−D present the UC charge and discharge sig-
nals through the UC voltage control loop. The schematic
diagram of the strategy is represented in Figure 16(a).
3) FLC STRATEGY
This strategy demonstrates a considerable dynamic response
performance under load variations and controls the power
flow from various sources [79]. The FLC algorithm offers
an effective power management system classifying the vehi-
cle load into three sections: steady-state, intermediate, and
transient. The FC provides the steady-state power while the
battery offers intermediate power, and the UC utilizes the
power during acceleration and deceleration of the vehicle.
This strategy uses two bi-directional DC/DC converters and
a boost DC/DC converter. The system is simulated in MAT-
LAB/Simulink, SimPowerSystem specifying mathematical
and electrical modeling and visualizing the design in different
situations like DC bus voltage, the SOC of UC and battery,
load power variations, and so on. FLC algorithm employees
FLC toolbox and ADVISOR result in the proposed strategy’s
validity under different parameter variations. This strategy
presents a practical design and implementation of the FLC
system, maintaining DC bus voltage in a specific range and
allowing power consumption economically.
4) WAVELET AND FLC
Wavelet and FLC based on the wavelet and fuzzy logic
algorithm that focuses on developing the unit’s overall fuel
economy and efficiency are presented in [80]. The pro-
posed system considers FC as the primary source and bat-
tery and UC as the secondary sources. Three DC/DC con-
verters are used with the DC bus. Wavelet and fuzzy logic
algorithm visualizes mathematical models and opens a new
era for improving vehicle performance. The whole system
is implemented in MATLAB/Simulink and SimPowerSys-
tem environment. The overall performance of the scheme
indicates better understanding compared to other strategies.
The schematic diagram of the control strategy is illustrated in
Figure 16(b).
5) OPTIMAL/FLATNESS BASED CONTROL
An optimal/flatness-based control is presented in [81]. The
strategy includes PMP, which uses the Euler-Lagrange equa-
tion. Like other methods, this system is also implemented
in MATLAB/Simulink environment and results in optimal
power consumption. Battery and UC fulfill the necessary
power requirements. The constancy of DC bus voltage is
successfully sustained.
6) GENETIC ALGORITHM AND PARETOFRONT ANALYSIS
A genetic algorithm and Pareto front analysis are given
in [82]. A small-scale experimental platform is introduced to
validate the suggested algorithm.
This algorithm applied in the FC/battery/SC HEV config-
uration intends to overcome the optimization problem’s dif-
ficulties. The SOC of the battery and SC state the time-based
characteristics of these sources.
The outcomes of this process ensure optimal fuel economy
and enlarge the lifetime of sources. For the proposed strategy,
the following relations are demonstrated as
ufc (t) = Kp,b xb,ref − xb (t)

+ Ki,b
Z t
0
(xb,ref − xb(τ))dτ (57)
ub (t) = Kp,sc(xsc,ref − xsc (t)) (58)
PI controller governs the state of battery around a con-
stant reference xb,ref and proportional controller governs
the state of SC around a constant reference xsc,ref . ufc and
ub present the output of the FC and battery, respectively.
The strategy also introduces some relations between various
51888 VOLUME 9, 2021

parameters:
xsc,ref (t) = 0.6 − Kref ,sc(
v (t)
vmax
)
2
(59)
Pb (t) =





+ ub (t) Pess (t) ub (t)0 and Pess(t) 0
− ub (t) Pess (t) ub (t)0 and Pess(t) 0
0 otherwise
(60)
Pess is the difference between Pdem and Pfcdc
7) AUTO-ADAPTIVE AND FILTERING BASED EMS
An auto-adaptive and filtering-based EMS is presented
in [83]. This approach includes both the FLC and the sliding
mode control. Secondary sources: battery, and SC, are used to
be functioned appropriately to meet the high-density power
demand. The effectiveness of this system is validated using
MATLAB/SimPowerSystem environment. The evaluation is
done in different driving cycles such as NEDC, NYCC,
Supplemental federal test procedure (SFTP), Light vehicle
test procedure (LVTP). The simulation ensures a safe and
exciting power management unit. The load current can be
calculated a
IL =
1.25
Vdc

0.5v2
Sf Cx + MgCr + M
dv
dt

v (61)
where v, Sf , Cx, M, Cr and g is the vehicle speed, the frontal
vehicle surface, the aerodynamic drag coefficient, the vehi-
cle mass, the rolling resistance coefficient, and gravitational
acceleration constant, respectively.
Sliding mode control provides different modes:
• The Attraction Condition: The control signal
u =
(
1, if S (x, t) 1
0, if S (x, t) 0
• The Existence Condition: The existence condition



lim
x→0+
Ṡ 0
lim
x→0−
Ṡ 0
Otherwise, ṠS 0
Ṡ is the sliding surface slop
• The Stability Condition: The stability condition



lim
x→0+
Ṡ 0
lim
x→0−
Ṡ 0
For All t ts where ts presents the
time required to reach the sliding surface.
8) INTELLIGENT EMS
An intelligent EMS is demonstrated in [84]. It is a multi-input
and multi-output-based concept that enhances the constancy
of the desired power level. The FC is attached with a unidi-
rectional DC/DC converter. The battery and SC each are con-
nected with a bidirectional DC/DC converter. SC contributes
to the peak power demand, and the battery intends to lessen
the power contrast between the required power and FC sup-
ply power. The process is simulated in MATLAB/Simulink
software.
9) POWER SHARING STRATEGY
A power-sharing strategy is presented in [85]. A combina-
tion of two FLC and one Haar WT is applied to the pro-
posed system. This approach’s main idea is to fulfill the
required power demand to attain higher efficiency and elimi-
nate power requirement fluctuations. This method introduces
the LF-LRV tramway as the driving cycle. The discrete WT
can be illustrated as
W (λ, u) =
Z
s (t)
1
√
λ
ψ

t − u
λ

dt,
λ = 2j
,
u = k2j
(62)
where W be the wavelet coefficient, ψ(t) is the mother
wavelet. λ is a scale parameter that governs the frequency
band, and u is the position parameter that regulates the size
of the time window.
10) OPTIMAL EMSs
An optimal EMS is represented in [86]. The basic idea behind
this strategy is to ensure an optimal operation situation.
Mainly it concerns the costing and efficiency of the whole
system. A multi-population genetic algorithm and an artificial
fish swarm algorithm have been applied, and the LF-LRV
tramway driving cycle has been used. The concept proves
the improved competence and low costing of this scheme.
The proposed method has three energy storage system that
presents different operating modes: the power demand Pd =
Pfuel cell when only the FC is provided. Pd = Pfuel cell + Psc
when FC and SC are employed and Pd = Pfuel cell + Psc +
Pbattery when all sources are introduced. Pd = Pfuel cell −
|Psc| presents the low power traction when FC and battery
operated. Again Pd = Pfuel cell − |Pbat| also represents the
low power traction when FC and SC operate.
A comparative analysis of different control strategies for
FC-Battery-UC configuration is presented in Table 13.
V. OUTCOME
Proper understanding of EV and HEV with associated
configurations of battery– FC, UC– FC, battery-UC, and
FC-battery-UC as ESSs are depicted in the proposed work.
The discussed control strategies utilize several simulation
platforms to test their efficacy. Figure 17 illustrates a
graphical representation of the number of various simula-
tion platforms utilized in the control techniques mentioned
above schemes. Most of the control scheme utilizes MAT-
LAB/Simulink. A few control schemes use ADVISOR and
dSPACE platform to continue research work. Only two con-
trol strategy includes PSAT software. A summary illustration
is also presented in Figure 18, shows the SC value utilized
in different hybrid storage system for EV applications. It is
VOLUME 9, 2021 51889

TABLE 13. Comparison among the above-mentioned control strategies for FC-Battery-UC configuration.
51890 VOLUME 9, 2021

TABLE 13. (Continued.) Comparison among the above-mentioned control strategies for FC-Battery-UC configuration.
FIGURE 17. A numerical overview of simulation platforms for selected
control schemes.
seen that the control strategies utilized in the Battery-UC
configurations use a lower value of C, while the Battery-UC-
FC configurations use a higher value of capacitors. Overall,
the outcomes from the comprehensive review work are pre-
sented below.
• Rule and MPC based techniques for battery-UC HESS
minimize energy losses and utilize UC for transient
power responses. The techniques limit system flexibility,
increase maintenance cost, generate current harmonics,
and increase operating time. Real-time-based EMS for
battery-UC HESS provides the DC-link stability and
enhances the dynamic responses. The scheme presents
complicated calculations and results in an error for
imperfect weight sets. Fuzzy logic-based EMS limits the
number of input variables and prolongs the run time.
The analysis of MPC techniques suggest being a better
platform than other nonlinear MPC techniques. Fuzzy
logic EMS acts as more reliable, flexible, efficient than
others, whereas MPC minimizes losses.
• Novel range-extended-based EMS for FC-battery
HESS ensures optimal fuel consumption and enhances
battery lifetime. The strategy is uneconomic in battery
SOC compensation. The proposed optimal EMSs pro-
vide optimal fuel consumption, maintain optimal sizing,
and improve system efficiency. Inefficient battery SOC
controlling and FC insensitiveness are the drawbacks
of these techniques. Fuzzy logic-based EMS ensures
a dynamic and robust operation while it limits the FC
output power. Rule-based EMS presents optimal per-
formance, optimal fuel consumption, and proper SOC
controlling while it does not consider driving cycles.
Rule-based EMS is preferred over others as it considers
the optimal controlling of battery SOC and optimal fuel
consumption.
• EMR and inversion-based control for FC-UC HESS
is inefficient for controlling UC SOC due to power
loss. The nonlinear control scheme can not provide
global stability and increases costs. Linear and sliding
control techniques provide a fast response for FC cur-
rent and ensure robust performance, although it some-
times presents undesirable performances. The method
is preferable to others as it maintains optimal power
distribution and provides system stability.
• Fuzzy logic-based EMS for FC-battery-UC is the flexi-
ble platform that minimizes fuel consumption and maxi-
mizes system efficiency. It controls battery and UC SOC
level in a specific range, but sometimes it faces undesir-
able SOC fluctuations and high run time. MPC-based
EMS can predict system variables accurately through it
creates a complex system and prolongs the execution
time. Intelligent EMS ensures a stable system with min-
imum power consumption and optimal power distribu-
tion, whereas PI controller may increase costs. Fuzzy
logic-based EMS is more efficient than others for its
flexibility, minimum consumption, and SOC control-
ling.
• The main focus of research in this field should be on
manufacturing batteries with more capacity and better-
ment in power densities. FCs should get free from fuel
injection complexities. Design of SCs should be done
with the availability of charging stations to keep in mind
for the system’s more efficient performance.
VOLUME 9, 2021 51891

FIGURE 18. Various SC bank utilized in different control strategies of three different HES configurations.
VI. CONCLUSION
This paper shows an analytical study over different HEV con-
trol strategies thoroughly for four configurations: battery-UC,
FC-UC, FC-battery, and FC-battery-UC. The study presents
a relative analysis among different control strategies for dif-
ferent configurations based on objectives, control aspects,
operating conditions, fuel consumptions, dynamic responses,
battery lifetime, etc. The relative analysis suggests the fuzzy
logic-based EMS as more efficient and flexible than others
for battery-UC configuration. Rule-based EMS is preferable
for battery-FC configuration as it handles battery SOC level
at a specific range and minimizes fuel consumption. FC-UC
HESS-based linear and sliding technique is efficient for opti-
mal power-sharing and system stability. Fuzzy logic ESS for
FC-battery-UC HESS minimizes fuel consumption, controls
battery and UC SOC, and provides flexibility. The presented
work suggests that the development of EV charging stations
and the possibility of renting charges between two HEVs may
be a promising topic for future research. Further research can
be carried out on developing an efficient switching algorithm
for different sources in EV. The enhancement of driving
cycles in HEV operations can be emphasized to maintain
optimal function.
ACKNOWLEDGMENT
The opinions, findings and conclusions or recommendations
expressed in this material are those of the authors and do
not necessarily reflect the views of the Science Foundation
Ireland.
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AMIT KUMER PODDER received the B.S. and
M.S degrees from the Khulna University of
Engineering Technology, Khulna, Bangladesh,
in 2016 and 2019, respectively. He joined as a
Lecturer with the Department of Electrical and
Electronic Engineering, Khulna University of
Engineering and Technology, in 2017, where he is
currently serving as an Assistant Professor. As an
author, he has published 11 peer-reviewed jour-
nal articles and 15 conference papers in several
IEEE co-sponsored international conferences. His research interests include
renewable energy integration, intelligent control systems, virtual power
plant, and smart micro-grid systems. He achieved the Prime Minister Gold
Medal and the University Gold Medal for outstanding performance at the
undergraduate level. He also serves as a reviewer for two prestigious journals,
namely, Renewable and Sustainable Energy Reviews (Elsevier) and IET
Renewable Power Generation.
OISHIKHA CHAKRABORTY (Student Member,
IEEE) was born in Chittagong, Bangladesh. She
is currently pursuing the B.Sc. degree in electri-
cal and electronic engineering with the Khulna
University of Engineering Technology, Khulna,
Bangladesh. Her research work is focused on
designing charging station for electric vehicle
applications along with solar energy employment
and energy storage systems.
SAYEMUL ISLAM is currently pursuing the
B.Sc. degree in electrical and electronic engineer-
ing (EEE) with the Khulna University of Engineer-
ing and Technology, Khulna, Bangladesh. He is
currently engaged in several research works, such
as virtual power plant (VPP), hybrid electric vehi-
cle, and MPPT techniques. His research interests
include power systems, renewable energy, electric
vehicle, and the IoT.
51894 VOLUME 9, 2021

NALLAPANENI MANOJ KUMAR received
the B.Tech. degree in electrical and elec-
tronics engineering from GITAM University,
Visakhapatnam, India, the M.Tech. degree in
renewable energy technologies from Karunya Uni-
versity, Coimbatore, India, and the M.A. degree
in environmental economics from Annamalai Uni-
versity (Directorate of Distance Education), Chi-
dambaram, India. He was a Research Fellow (Jr.)
at the University of Malaysia Pahang worked on
the project ‘‘Solar Photovoltaics as Urban Infrastructure.’’ He is currently
with the School of Energy and Environment, City University of Hong Kong,
Hong Kong. He has published 13 chapters in scientific books, more than
100 research articles in scientific journals, and more than 30 articles in
international conferences. His research mainly focuses on renewable energy,
building integrated photovoltaic systems, energy management, modeling and
performance investigation of energy systems, building energy optimization,
smart cities, new dimensions in solar energy, applications of Internet of
Things, and blockchain technology.
HASSAN HAES ALHELOU (Senior Member,
IEEE) is currently a Faculty Member of Tishreen
University, Lattakia, Syria. He is also with Uni-
versity College Dublin, Ireland. He has published
more than 100 research papers in high-quality
peer-reviewed journals and international confer-
ences. He has participated in more than 15 indus-
trial projects. His major research interests include
power systems, power system dynamics, power
system operation and control, dynamic state esti-
mation, frequency control, smart grids, microgrids, demand response, load
shedding, and power system protection. He was included in the 2018 and
2019 Publons list of the top 1% best reviewer and researchers in the field of
engineering. He was a recipient of the Outstanding Reviewer Award from
Energy Conversion and Management journal, in 2016; ISA Transactions
journal, in 2018; Applied Energy journal, in 2019; and many other awards.
He was also a recipient of the Best Young Researcher in the Arab Student
Forum Creative among 61 researchers from 16 countries at Alexandria
University, Egypt, in 2011. He has also performed more than 600 reviews
for high prestigious journals, including IEEE TRANSACTIONS ON INDUSTRIAL
INFORMATICS, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, Energy Con-
version and Management, Applied Energy, the International Journal of
Electrical Power Energy Systems.
VOLUME 9, 2021 51895

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